


import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling






def 						a__			(				A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     int					=					old_name

			if "patch_embed" in old_name:
						SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     int					=					old_name.split('.'				)

						if layer == "0":
									SCREAMING_SNAKE_CASE_		:     List[Any]					=					old_name.replace('0',      'convolution1'				)
						elif layer == "1":
									SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					old_name.replace('1',      'batchnorm_before'				)
						elif layer == "3":
									SCREAMING_SNAKE_CASE_		:     str					=					old_name.replace('3',      'convolution2'				)
						else:
									SCREAMING_SNAKE_CASE_		:     int					=					old_name.replace('4',      'batchnorm_after'				)

			if "network" in old_name and re.search(r'\d\.\d',      _A				):
						SCREAMING_SNAKE_CASE_		:     Tuple					=					r'\b\d{2}\b'
						if bool(re.search(_A,      _A				)				):
									SCREAMING_SNAKE_CASE_		:     Any					=					re.search(r'\d\.\d\d.',      _A				).group()
						else:
									SCREAMING_SNAKE_CASE_		:     Tuple					=					re.search(r'\d\.\d.',      _A				).group()
						if int(match[0]				) < 6:
									SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					old_name.replace(_A,      ''				)
									SCREAMING_SNAKE_CASE_		:     Dict					=					trimmed_name.replace('network',      match[0] + '.meta4D_layers.blocks.' + match[2:-1]				)
									SCREAMING_SNAKE_CASE_		:     Tuple					=					'intermediate_stages.' + trimmed_name
						else:
									SCREAMING_SNAKE_CASE_		:     Dict					=					old_name.replace(_A,      ''				)
									if int(match[2]				) < num_meta4D_last_stage:
												SCREAMING_SNAKE_CASE_		:     Tuple					=					trimmed_name.replace('network',      'meta4D_layers.blocks.' + match[2]				)
									else:
												SCREAMING_SNAKE_CASE_		:     Tuple					=					str(int(match[2]				) - num_meta4D_last_stage				)
												SCREAMING_SNAKE_CASE_		:     Tuple					=					trimmed_name.replace('network',      'meta3D_layers.blocks.' + layer_index				)
												if "norm1" in old_name:
															SCREAMING_SNAKE_CASE_		:     str					=					trimmed_name.replace('norm1',      'layernorm1'				)
												elif "norm2" in old_name:
															SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					trimmed_name.replace('norm2',      'layernorm2'				)
												elif "fc1" in old_name:
															SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					trimmed_name.replace('fc1',      'linear_in'				)
												elif "fc2" in old_name:
															SCREAMING_SNAKE_CASE_		:     str					=					trimmed_name.replace('fc2',      'linear_out'				)

									SCREAMING_SNAKE_CASE_		:     str					=					'last_stage.' + trimmed_name

			elif "network" in old_name and re.search(r'.\d.',      _A				):
						SCREAMING_SNAKE_CASE_		:     Any					=					old_name.replace('network',      'intermediate_stages'				)

			if "fc" in new_name:
						SCREAMING_SNAKE_CASE_		:     Any					=					new_name.replace('fc',      'convolution'				)
			elif ("norm1" in new_name) and ("layernorm1" not in new_name):
						SCREAMING_SNAKE_CASE_		:     Any					=					new_name.replace('norm1',      'batchnorm_before'				)
			elif ("norm2" in new_name) and ("layernorm2" not in new_name):
						SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					new_name.replace('norm2',      'batchnorm_after'				)
			if "proj" in new_name:
						SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					new_name.replace('proj',      'projection'				)
			if "dist_head" in new_name:
						SCREAMING_SNAKE_CASE_		:     int					=					new_name.replace('dist_head',      'distillation_classifier'				)
			elif "head" in new_name:
						SCREAMING_SNAKE_CASE_		:     int					=					new_name.replace('head',      'classifier'				)
			elif "patch_embed" in new_name:
						SCREAMING_SNAKE_CASE_		:     Any					=					'efficientformer.' + new_name
			elif new_name == "norm.weight" or new_name == "norm.bias":
						SCREAMING_SNAKE_CASE_		:     Any					=					new_name.replace('norm',      'layernorm'				)
						SCREAMING_SNAKE_CASE_		:     str					=					'efficientformer.' + new_name
			else:
						SCREAMING_SNAKE_CASE_		:     Dict					=					'efficientformer.encoder.' + new_name

			return new_name






def 						a__			(				A__,      A__				):
			for key in checkpoint.copy().keys():
						SCREAMING_SNAKE_CASE_		:     str					=					checkpoint.pop(_A				)
						SCREAMING_SNAKE_CASE_		:     Dict					=					val

			return checkpoint






def 						a__			(				):
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					'http://images.cocodataset.org/val2017/000000039769.jpg'
			SCREAMING_SNAKE_CASE_		:     Dict					=					Image.open(requests.get(_A,      stream=_A				).raw				)

			return image






def 						a__			(				A__,      A__,      A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					torch.load(_A,      map_location='cpu'				)['model']
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					EfficientFormerConfig.from_json_file(_A				)
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					EfficientFormerForImageClassificationWithTeacher(_A				)
			SCREAMING_SNAKE_CASE_		:     int					=					'_'.join(checkpoint_path.split('/'				)[-1].split('.'				)[0].split('_'				)[:-1]				)

			SCREAMING_SNAKE_CASE_		:     Any					=					config.depths[-1] - config.num_metaad_blocks + 1
			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					convert_torch_checkpoint(_A,      _A				)

			model.load_state_dict(_A				)
			model.eval()

			SCREAMING_SNAKE_CASE_		:     Any					=					{
			    'bilinear': PILImageResampling.BILINEAR,
			    'bicubic': PILImageResampling.BICUBIC,
			    'nearest': PILImageResampling.NEAREST,
			}

			# prepare image
			SCREAMING_SNAKE_CASE_		:     Dict					=					prepare_img()
			SCREAMING_SNAKE_CASE_		:     str					=					2_5_6
			SCREAMING_SNAKE_CASE_		:     Tuple					=					2_2_4
			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					EfficientFormerImageProcessor(
			    size={'shortest_edge': image_size},      crop_size={'height': crop_size, 'width': crop_size},      resample=pillow_resamplings['bicubic'],      )
			SCREAMING_SNAKE_CASE_		:     Any					=					processor(images=_A,      return_tensors='pt'				).pixel_values

			# original processing pipeline
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					Compose(
			    [
			        Resize(_A,      interpolation=pillow_resamplings['bicubic']				),
			        CenterCrop(_A				),
			        ToTensor(),
			        Normalize(_A,      _A				),
			    ]				)
			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					image_transforms(_A				).unsqueeze(0				)

			assert torch.allclose(_A,      _A				)

			SCREAMING_SNAKE_CASE_		:     int					=					model(_A				)
			SCREAMING_SNAKE_CASE_		:     str					=					outputs.logits

			SCREAMING_SNAKE_CASE_		:     Dict					=					(1, 1_0_0_0)

			if "l1" in model_name:
						SCREAMING_SNAKE_CASE_		:     str					=					torch.Tensor(
						    [-0.13_12, 0.43_53, -1.04_99, -0.51_24, 0.41_83, -0.67_93, -1.37_77, -0.08_93, -0.73_58, -2.43_28]				)
						assert torch.allclose(logits[0, :1_0],      _A,      atol=1E-3				)
						assert logits.shape == expected_shape
			elif "l3" in model_name:
						SCREAMING_SNAKE_CASE_		:     Tuple					=					torch.Tensor(
						    [-1.31_50, -1.54_56, -1.25_56, -0.84_96, -0.71_27, -0.78_97, -0.97_28, -0.30_52, 0.37_51, -0.31_27]				)
						assert torch.allclose(logits[0, :1_0],      _A,      atol=1E-3				)
						assert logits.shape == expected_shape
			elif "l7" in model_name:
						SCREAMING_SNAKE_CASE_		:     int					=					torch.Tensor(
						    [-1.02_83, -1.41_31, -0.56_44, -1.31_15, -0.57_85, -1.20_49, -0.75_28, 0.19_92, -0.38_22, -0.08_78]				)
						assert logits.shape == expected_shape
			else:
						raise ValueError(
						    F'''Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7'''				)

			# Save Checkpoints
			Path(_A				).mkdir(exist_ok=_A				)
			model.save_pretrained(_A				)
			print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''				)
			processor.save_pretrained(_A				)
			print(F'''Processor successfuly saved at {pytorch_dump_path}'''				)

			if push_to_hub:
						print('Pushing model to the hub...'				)

						model.push_to_hub(
						    repo_id=F'''Bearnardd/{pytorch_dump_path}''',      commit_message='Add model',      use_temp_dir=_A,      )
						processor.push_to_hub(
						    repo_id=F'''Bearnardd/{pytorch_dump_path}''',      commit_message='Add image processor',      use_temp_dir=_A,      )


if __name__ == "__main__":
				lowerCAmelCase__			:      Optional[Any]								=argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--pytorch_model_path',
				    default=None,
				    type=str,
				    required=True,
				    help='Path to EfficientFormer pytorch checkpoint.',
				)
				parser.add_argument(
				    '--config_file',
				    default=None,
				    type=str,
				    required=True,
				    help='The json file for EfficientFormer model config.',
				)
				parser.add_argument(
				    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
				)

				parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
				parser.add_argument(
				    '--no-push_to_hub',
				    dest='push_to_hub',
				    action='store_false',
				    help='Do not push model and image processor to the hub',
				)
				parser.set_defaults(push_to_hub=True)

				lowerCAmelCase__			:      Optional[int]								=parser.parse_args()
				convert_efficientformer_checkpoint(
				    checkpoint_path=args.pytorch_model_path,
				    efficientformer_config_file=args.config_file,
				    pytorch_dump_path=args.pytorch_dump_path,
				    push_to_hub=args.push_to_hub,
				)









__UpperCamelCase     		=							2_5_6
# Modulus to hash a string
__UpperCamelCase     		=							1_0_0_0_0_0_3
def 							UpperCamelCase_(					_A :str					,  _A :str	)->		bool:
			UpperCamelCase__       =							len(_A	)
			UpperCamelCase__       =							len(_A	)
			if p_len > t_len:
						return False

			UpperCamelCase__       =							0
			UpperCamelCase__       =							0
			UpperCamelCase__       =							1

			# Calculating the hash of pattern and substring of text
			for i in range(_A	):
						UpperCamelCase__       =							(ord(pattern[i]	) + p_hash * alphabet_size) % modulus
						UpperCamelCase__       =							(ord(text[i]	) + text_hash * alphabet_size) % modulus
						if i == p_len - 1:
									continue
						UpperCamelCase__       =							(modulus_power * alphabet_size) % modulus

			for i in range(0					,  t_len - p_len + 1	):
						if text_hash == p_hash and text[i : i + p_len] == pattern:
									return True
						if i == t_len - p_len:
									continue
						# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
						UpperCamelCase__       =							(
						    (text_hash - ord(text[i]	) * modulus_power) * alphabet_size
						    + ord(text[i + p_len]	)
						) % modulus
			return False
def 							UpperCamelCase_(					)->		None:
			UpperCamelCase__       =							"abc1abc12"
			UpperCamelCase__       =							"alskfjaldsabc1abc1abc12k23adsfabcabc"
			UpperCamelCase__       =							"alskfjaldsk23adsfabcabc"
			assert rabin_karp(_A					,  _A	) and not rabin_karp(_A					,  _A	)

			# Test 2)
			UpperCamelCase__       =							"ABABX"
			UpperCamelCase__       =							"ABABZABABYABABX"
			assert rabin_karp(_A					,  _A	)

			# Test 3)
			UpperCamelCase__       =							"AAAB"
			UpperCamelCase__       =							"ABAAAAAB"
			assert rabin_karp(_A					,  _A	)

			# Test 4)
			UpperCamelCase__       =							"abcdabcy"
			UpperCamelCase__       =							"abcxabcdabxabcdabcdabcy"
			assert rabin_karp(_A					,  _A	)

			# Test 5)
			UpperCamelCase__       =							"Lü"
			UpperCamelCase__       =							"Lüsai"
			assert rabin_karp(_A					,  _A	)
			UpperCamelCase__       =							"Lue"
			assert not rabin_karp(_A					,  _A	)
			print("Success."	)


if __name__ == "__main__":
						test_rabin_karp()


